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Sewoong Hwang
Graduate School of Information, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea

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Journal article
Published: 13 March 2021 in Sustainability
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This study examines technology effectiveness for industry demand in which artificial intelligence (AI) is applied in the financial sector. It summarizes prior studies on chatbot and customer service and investigates theories on acceptance attitudes for innovative technologies. By setting variables, the study examines bank revenue methodologically and assesses the impact of customer service and chatbot on bank revenues through customer age classification. The results indicate that new product-oriented funds or housing subscription savings are more suitable for purchase through customer service than through chatbot. However, services for existing products through chatbot positively affect banks’ net income. When classified by age, purchases by the majority age group in the channel positively affect bank profits. Finally, there is a tendency to process small banking transactions through the chatbot system, which saves transaction and management costs, positively affecting profits. Through empirical analysis, we first examine the effect of an AI-based chatbot system implemented to strengthen financial soundness and suggest policy alternatives. Second, we use banking data to increase the study’s real-life applicability and prove that problems in customer service can be solved through a chatbot system. Finally, we investigate how resistance to technology can be reduced and efficiently accommodated.

ACS Style

Sewoong Hwang; Jonghyuk Kim. Toward a Chatbot for Financial Sustainability. Sustainability 2021, 13, 3173 .

AMA Style

Sewoong Hwang, Jonghyuk Kim. Toward a Chatbot for Financial Sustainability. Sustainability. 2021; 13 (6):3173.

Chicago/Turabian Style

Sewoong Hwang; Jonghyuk Kim. 2021. "Toward a Chatbot for Financial Sustainability." Sustainability 13, no. 6: 3173.

Journal article
Published: 13 November 2020 in Electronics
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Seoul Metropolitan City’s buses cater to more than 50% of the average daily public transportation use, and they are the most important transportation mode in Korea, together with the subway. Since 2004, all public transportation records of passengers have been stored in Seoul, using smart transportation cards. This study explores the environmental and psychological factors in implementing a smart transportation system. We analyze the switching behavior of traffic users according to traffic congestion time and number of transfers based on public transportation data and show that bus-use behavior differs according to the traffic information of users and the degree of traffic congestion. Information-based switching behavior of people living near bus stops induces people to change routes during traffic congestion. However, in non-congested situations, the original routes are used. These results can guide the formulation of policy measures on bus routes. We made it possible to continuously change the routes for certain buses, which were temporarily implemented due to traffic congestion. Moreover, we added a service that posts the estimated arrival time to major stops while reflecting real-time traffic conditions in addition to the bus location and arrival time information through the global positioning system.

ACS Style

Zoonky Lee; Sewoong Hwang; Jonghyuk Kim. Optimal Planning of Real-Time Bus Information System for User-Switching Behavior. Electronics 2020, 9, 1903 .

AMA Style

Zoonky Lee, Sewoong Hwang, Jonghyuk Kim. Optimal Planning of Real-Time Bus Information System for User-Switching Behavior. Electronics. 2020; 9 (11):1903.

Chicago/Turabian Style

Zoonky Lee; Sewoong Hwang; Jonghyuk Kim. 2020. "Optimal Planning of Real-Time Bus Information System for User-Switching Behavior." Electronics 9, no. 11: 1903.

Journal article
Published: 20 November 2019 in Sustainability
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The application of smart city technologies requires new data analysis methods to interpret the voluminous data collected. In this study, we first analyzed the transfer behavior of subway pedestrians using the fingerprinting technique using data collected by more than 100 MAC (Media Access Control) ID sensors installed in a congested subway station serving two subway lines. We then developed a model that employs an AI (Artificial Intelligence)-based methodology, the cumulative visibility of moving objects (CVMO), to present the data in such a manner that it could be used to address pedestrian flow issues in this real-world implementation of smart city technology. The MAC ID location data collected during a three-month monitoring period were mapped using the fingerprinting wireless location sensing method to display the congestion situation in real time. Furthermore we developed a model that can inform immediate response to identified conditions. In addition, we formulated several schemes for disbursing congestion and improving pedestrian flow using behavioral economics, and then confirmed their effectiveness in a follow-up monitoring period. The proposed pedestrian flow analysis method cannot only solve pedestrian congestion, but can also help to prevent accidents and maintain public order.

ACS Style

Sewoong Hwang; Zoonky Lee; Jonghyuk Kim. Real-Time Pedestrian Flow Analysis Using Networked Sensors for a Smart Subway System. Sustainability 2019, 11, 6560 .

AMA Style

Sewoong Hwang, Zoonky Lee, Jonghyuk Kim. Real-Time Pedestrian Flow Analysis Using Networked Sensors for a Smart Subway System. Sustainability. 2019; 11 (23):6560.

Chicago/Turabian Style

Sewoong Hwang; Zoonky Lee; Jonghyuk Kim. 2019. "Real-Time Pedestrian Flow Analysis Using Networked Sensors for a Smart Subway System." Sustainability 11, no. 23: 6560.